Abstract
Engineering simulation provides better designed products by allowing many options to be quickly explored and tested. In that context, the computational time is a strong issue because using high-fidelity direct resolution solvers is not always suitable. Metamodels are commonly considered to explore design options without computing every possible combination of parameters, but if the behavior is nonlinear, a large amount of data is required to build this metamodel. A possibility is to use further data sources to generate a multi-fidelity surrogate model by using model reduction. Model reduction techniques constitute one of the tools to bypass the limited calculation budget by seeking a solution to a problem on a reduced-order basis (ROB). The purpose of this study is an online method for generating a multi-fidelity metamodel nourished by calculating the quantity of interest from the basis generated on-the-fly with the LATIN-PGD framework for elasto-viscoplastic problems. Low-fidelity (LF) fields are obtained by stopping the solver before convergence, and high-fidelity (HF) information is obtained with converged solutions. In addition, the solver ability to reuse information from previously calculated PGD basis is exploited.
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Nachar, S., Boucard, PA., Néron, D., Nackenhorst, U., Fau, A. (2020). Multi-fidelity Metamodels Nourished by Reduced Order Models. In: Wriggers, P., Allix, O., Weißenfels, C. (eds) Virtual Design and Validation. Lecture Notes in Applied and Computational Mechanics, vol 93. Springer, Cham. https://doi.org/10.1007/978-3-030-38156-1_4
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